Covariance and PCA for categorical variables

Hirotaka Niitsuma, Takashi Okada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Covariances from categorical variables are defined using a regular simplex expression for categories. The method follows the variance definition by Gini, and it gives the covariance as a solution of simultaneous equations using the Newton method. The calculated results give reasonable values for test data. A method of principal component analysis (RS-PCA) is also proposed using regular simplex expressions, which allows easy interpretation of the principal components.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages523-528
Number of pages6
Volume3518 LNAI
Publication statusPublished - 2005
Externally publishedYes
Event9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005 - Hanoi, Viet Nam
Duration: May 18 2005May 20 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3518 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005
CountryViet Nam
CityHanoi
Period5/18/055/20/05

Fingerprint

Categorical variable
Passive Cutaneous Anaphylaxis
Newton-Raphson method
Principal component analysis
Simultaneous equations
Principal Components
Newton Methods
Principal Component Analysis
Interpretation

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Niitsuma, H., & Okada, T. (2005). Covariance and PCA for categorical variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3518 LNAI, pp. 523-528). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3518 LNAI).

Covariance and PCA for categorical variables. / Niitsuma, Hirotaka; Okada, Takashi.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI 2005. p. 523-528 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3518 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Niitsuma, H & Okada, T 2005, Covariance and PCA for categorical variables. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3518 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3518 LNAI, pp. 523-528, 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005, Hanoi, Viet Nam, 5/18/05.
Niitsuma H, Okada T. Covariance and PCA for categorical variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI. 2005. p. 523-528. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Niitsuma, Hirotaka ; Okada, Takashi. / Covariance and PCA for categorical variables. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3518 LNAI 2005. pp. 523-528 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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